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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Medical Physicsarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Medical Physics
Article . 2024 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
https://doi.org/10.1117/12.300...
Article . 2024 . Peer-reviewed
Data sources: Crossref
Medical Physics
Article . 2025
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Optimal weighting strategies for maximizing contrast‐to‐noise ratio in photon counting CT images

Authors: Yirong, Yang; Sen, Wang; Grant M, Stevens; Jiahua, Fan; Adam S, Wang;

Optimal weighting strategies for maximizing contrast‐to‐noise ratio in photon counting CT images

Abstract

AbstractBackgroundPhoton counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast‐to‐noise ratio (CNR) through optimal weighting of their spectral measurements.PurposeThis study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy‐weighting strategies: pre‐log weighting, post‐log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy‐weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques.MethodsWe defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low‐contrast tasks using three energy‐weighting strategies: pre‐log weighting (weighted sum of energy bin counts), post‐log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection‐ and image‐domain CNR performance of these energy‐weighting strategies. Additionally, the total counts method (a special case of pre‐log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge‐on‐irradiated silicon PCCT prototype to evaluate the image‐domain CNR performance of these energy‐weighting strategies.ResultsThe results show that pre‐log, post‐log, and MD weighting strategies generate approximately equal projection‐domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy‐weighting strategies also generate approximately equal image‐domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre‐log weighting generates the highest image‐domain CNR for an off‐center contrast task location or inhomogeneous phantoms while also outperforming the total counts method.ConclusionsWe derived the expression of projection‐domain maximum achievable CNR using three energy‐weighting strategies. Our results suggest that using pre‐log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off‐center region of interests (ROIs).

Related Organizations
Keywords

Photons, Phantoms, Imaging, Image Processing, Computer-Assisted, Signal-To-Noise Ratio, Tomography, X-Ray Computed, Monte Carlo Method

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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